To calculate standardized regression coefficient in R, first you need to scale data using scale() function and then calculate linear regression using lm() function.

The following method shows how you can do it with syntax.

Method 1: Use scale() and lm() Function

scaled_data <- scale(data)

lm(scaled_data)


The following example shows how to calculate regression coefficient in R.

## Using scale() and lm() Function

Let’s see how we can calculate standard regression coefficient in R for dataframe:

# Create data frame
df <- data.frame(Machine_name=c("A","B","C","D","E","F","G","H"),
Pressure=c(12.39,11.25,12.15,13.48,13.78,12.89,12.21,12.58),
Temperature=c(78,89,85,84,81,79,77,85),
Status=c(1,1,0,1,0,0,1,0))

# Standardized each column and fit regression model
model_std <- lm(scale(Status) ~ scale(Pressure) + scale(Temperature), data=df)

# Turn off scientific notation
options(scipen=999)

# Print model summary
summary(model_std)


Output:

Call:
lm(formula = scale(Status) ~ scale(Pressure) + scale(Temperature),
data = df)

Residuals:
1       2       3       4       5       6       7       8
0.6089  0.5745 -1.0267  1.4966 -0.3706 -0.9454  0.4618 -0.7990
attr(,"scaled:center")
[1] 0.5
attr(,"scaled:scale")
[1] 0.5345

Coefficients:
Estimate             Std. Error t value Pr(>|t|)
(Intercept)        -0.0000000000000001103  0.3833337991214945673   0.000    1.000
scale(Pressure)    -0.4232585044554402209  0.4388721238096346955  -0.964    0.379
scale(Temperature) -0.2156345776725547281  0.4388721238096346955  -0.491    0.644

Residual standard error: 1.084 on 5 degrees of freedom
Multiple R-squared:  0.1603,	Adjusted R-squared:  -0.1756
F-statistic: 0.4773 on 2 and 5 DF,  p-value: 0.6461


Here in the above code we standardized each column and fit regression model.Then we turn off scientific notations and show the model summary.